In this section, we numerically investigate the performance of the stochastic map
filter over a range of examples.
We show that nonlinear configurations of the new filter can have
tracking performance consistently superior to that of the EnKF. We also show that
the stochastic map filter provides better approximations of the
true (Bayesian) filtering distribution.
We focus on the stochastic map filter because it can be implemented
using only convex optimization, regardless of any
nonlinear/non-Gaussian structure in the state-space model.
Numerical experiments with earlier versions of the deterministic map
filter can be found in related work by the authors [1]}, for the special case of
conditionally independent and local observations (see Appendix ).
Many aspects of our problem setup and our performance assessment follow [2]}, as detailed below.
